A Bayesian approach to improved estimation of causal effect predictiveness for a principal surrogate endpoint.

ABSTRACT: The literature on potential outcomes has shown that traditional methods for characterizing surrogate endpoints in clinical trials based only on observed quantities can fail to capture causal relationships between treatments, surrogates, and outcomes. Building on the potential-outcomes formulation of a principal surrogate, we introduce a Bayesian method to estimate the causal effect predictiveness (CEP) surface and quantify a candidate surrogate's utility for reliably predicting clinical outcomes. In considering the full joint distribution of all potentially observable quantities, our Bayesian approach has the following features. First, our approach illuminates implicit assumptions embedded in previously-used estimation strategies that have been shown to result in poor performance. Second,

SUBMITTER: Zigler CM 

PROVIDER: S-EPMC3860118 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature